模糊系统的微粒群并行聚类算法  

Parallel cluster algorithm based on fuzzy systemic particle swarm optimization

作　　者：王泽[1] 蔡焕夫[1] 高平安[1] 

机构地区：[1]广东金融学院计算科学与技术系,广州510520

出　　处：《计算机工程与应用》2011年第20期41-43,共3页Computer Engineering and Applications

基　　金：国家自然科学基金(No.61070232);广东省自然科学基金(No.06024881);广东金融学院校级课题(No.09xJ02-06)~~

摘　　要：为了解决大规模的数据聚类问题时需要的大量计算,提出了一种模糊系统的微粒群优化并行k-means聚类算法。该方法利用模糊规则,动态地调整微粒群惯性权重和加速因子,克服群体逐渐失去迁移性而停止进化的问题,保证群体多样性而避免陷入局部极小值。采用任务并行和部分异步通信模式,降低计算时间。实验结果表明,该算法在并行机群上运行时,加快了聚类算法的计算速度,提高了聚类质量。To solve large numbers of computations in the problem of large-scale data clustering, a particle swarm optimiza- tion of fuzzy systems in parallel k-means clustering algorithm is proposed to deal with this problem.The method adjusts dynamically inertia weight and acceleration factor of particle swarm optimization with fuzzy rules,the problems of particle mobility loss and the end of evolution can be dealt with successfully.the algorithm maintains individual diversity and solves the premature convergence problem.Task parallelization and partially asynchronous communication of the algorithm are employed to decrease computing time.The simulation experiments indicate the algorithm helps increase computing speed and improve the clustering quality.

关 键 词：并行聚类 模糊系统微粒群优化 任务并行 异步通信 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]